Distributed adaptive neural network constraint containment control for the benthic autonomous underwater vehicles

Multiple Autonomous Underwater Vehicles (AUV) can complete complex ocean exploration missions cooperatively. This paper proposes a containment control algorithm under time-varying constraints by using a neural network for control of multiple benthic AUVs which are called the Ocean Bottom Flying Node (OBFN) systems. The multiple OBFNs in the presence of nonlinear model uncertainties are under the directed topology. First, we define the auxiliary variable and low-order filter. The anti-windup saturation auxiliary system is constructed in presence of the input saturation. Further, the adaptive law and neural network are designed to compensate environmental disturbances and systems model uncertainties, respectively. Moreover, in order to ensure the control performance of OBFN, an exponential boundary constraint is imposed which could constrain the system error convergent rates and bounds. Lyapunov stability theorem and graph theory are used to prove that the multiple OBFN systems are uniformly ultimately bounded. Finally, simulation results for multiple OBFNs illustrate the effectiveness of the proposed algorithm.(c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a containment control algorithm using a neural network for multiple benthic AUVs under time-varying constraints. The algorithm compensates for environmental disturbances and system model uncertainties and ensures control performance with the aid of an exponential boundary constraint.